class_docstr: Knowledge distillation loops for training and evaluation. This module is independent of
  the model definition. This class inherits from the Pytorch Lightning's LightningModule:
  https://lightning.ai/docs/pytorch/stable/common/lightning_module.html
code: def _compute_rkd_loss(
      self,
      student_output: dict,
      teacher_output: dict,
  ):
      student_result = student_output[self.student_model.prefix][
          FEATURES
      ].squeeze(dim=1)
      teacher_result = teacher_output[self.teacher_model.prefix][
          FEATURES
      ].squeeze(dim=1)
  
      student_result = self.output_feature_adaptor(student_result)
  
      loss = self.rkd_loss_func(
          feature_student=student_result,
          feature_teacher=teacher_result,
      )
  
      return loss
  
imports: import logging
  from typing import Callable, List, Optional, Union
  import lightning.pytorch
  import torch
  import torch.nn.functional
  import torchmetrics
  from lightning.pytorch.utilities import grad_norm
  from omegaconf import DictConfig
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from torchmetrics.aggregation import BaseAggregator
  from ..constants import FEATURES, LOGITS, WEIGHT
  from ..models.utils import run_model
  from .utils import apply_layerwise_lr_decay, apply_single_lr, apply_two_stages_lr, get_lr_scheduler, get_optimizer
label_desc: Use image node_id 5 for calling the DistillerLitModule obj's underlying member method code with example usage: obj._compute_rkd_loss(student_output, teacher_output) and returns: loss